As organizations expand their use of artificial intelligence (AI), securing AI systems has become a growing cybersecurity priority. According to the World Economic Forum’s Global Cybersecurity Outlook 2026, 87% of respondents identified AI-related vulnerabilities as the fastest-growing cyber risk in 2025. The findings highlight the need for professionals to understand emerging AI threats and implement security measures across the AI development lifecycle.
Johns Hopkins University (JHU), through its Engineering Executive and Professional Education division, offers the Certificate Program in AI Security, a nine-week online program designed to help professionals develop practical skills in securing AI systems, defending against AI-driven threats, and implementing responsible AI practices.
The program offers both code-based and no-code/low-code learning formats to accommodate technology professionals and senior leaders.
What Does the AI Security Certificate Program Cover?
The program focuses on securing AI systems from design and development through deployment, operations, and governance. Its curriculum covers AI threat modeling, secure architecture, data integrity, supply chain security, model alignment, adversarial attacks, runtime guardrails, and AI governance.
The nine-week structure progresses through three modules: Secure AI Design and Threat Landscape; Securing the AI Build Lifecycle; and Deployment, Operations, and Governance. Preparatory learning in Python programming, AI fundamentals, and cybersecurity principles helps participants build the technical foundation required for the program.
Participants explore threats such as prompt injection, jailbreaks, data poisoning, model misuse, and attacks on AI agents. The curriculum also addresses model resilience, secure development practices, infrastructure security, continuous monitoring, and human oversight.
How Does the Program Help Professionals Secure AI Systems?
The program combines technical concepts with hands-on learning through labs, real-world case studies, and projects. Participants learn to identify vulnerabilities, assess model robustness, protect AI data pipelines, and apply security controls to AI applications.
Hands-on projects connect AI security concepts with practical applications.
In the code-based format, project examples include identifying and addressing backdoors introduced through data poisoning, evaluating model security and bias, and testing whether AI agents are vulnerable to tool misuse.
The no-code/low-code format includes projects involving secure AI workflows, the creation of an AI Bill of Materials, and the evaluation of third-party AI models using model cards. These activities address security assessment and risk management without placing the same emphasis on programming.
The program also includes masterclasses on attacks on AI, secure architecture and deployment of AI agents, and Anthropic’s AI ecosystem. A self-paced module on Claude-based AI workflows introduces agentic workflow design, tool integration, and responsible AI principles.
Program Delivery and Learning Experience
The nine-week online program combines recorded lectures, monthly masterclasses led by Johns Hopkins University faculty, and weekly live mentorship sessions with industry experts.
Participants work through practical exercises, case studies, and projects, with additional learning support from the Program Support Team and Great Learning Community.
Participants also develop a shareable e-portfolio documenting their applied learning and project work. Depending on the learning format, the curriculum introduces tools and technologies such as Python, Google Colab, Hugging Face, OWASP Threat Dragon, Garak, Claude, and the OpenAI API.
Access to certain tools may require separate subscriptions or additional fees.
Faculty and Industry Expertise
The program draws on the expertise of Johns Hopkins University faculty and industry professionals. Featured faculty include Dr. Ian McCulloh, Director of AI Executive and Professional Education at JHU; Kiran Chittargi, Adjunct Faculty in Computer Science at JHU; Dr. Pedro Rodriguez, Faculty in the JHU Artificial Intelligence Program; and Dr. Art Sedighi, Adjunct Faculty at JHU.
Industry mentorship includes professionals such as Aniket Amdekar, General Manager at Great Learning, and Ivan Marchany, Cybersecurity Engineer at Raymond James.
Who Is the Program For?
The Certificate Program in AI Security addresses different responsibilities across AI development, cybersecurity, and organizational governance. The program is designed for:
- AI and data professionals who build, implement, or deploy AI solutions and want to strengthen their ability to secure systems across the lifecycle.
- Cybersecurity and technology professionals looking to extend their expertise to large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and AI workflows.
- Senior leaders, CXOs, and governance professionals responsible for risk management, compliance, and evaluating secure AI architectures.
Certificate and Continuing Education Units
Upon successful completion, participants earn a Certificate of Completion from Johns Hopkins University and 6.3 Continuing Education Units (CEUs), representing 63 learning hours.
About Johns Hopkins University
Johns Hopkins University is a research-focused institution with expertise spanning artificial intelligence, cybersecurity, and engineering. Its interdisciplinary approach brings together academic research and practical applications to address emerging challenges in AI security.
About Great Learning
Great Learning is the program partner supporting the delivery of the Certificate Program in AI Security in collaboration with Johns Hopkins University. It offers professional and higher education programs across technology, data, and business in collaboration with leading academic institutions worldwide.
